Orthogonal Attention

Orthogonal attention is a modified attention mechanism in neural networks designed to improve model performance and efficiency by enforcing orthogonality constraints on attention matrices. Current research focuses on applying orthogonal attention within various architectures, including transformers, to address challenges in diverse fields like time series forecasting, image segmentation, and federated learning. This approach aims to reduce redundancy, enhance generalization, and improve the interpretability of attention mechanisms, leading to more accurate and efficient models across a range of applications. The resulting improvements in model performance and efficiency are significant for resource-constrained environments and complex tasks.